Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery performance prediction. One of the methods includes actions of receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property of at least two battery tests. Each battery test includes applying pulses on the battery cell during a battery cycle. The battery test data is provided as input to a machine learning system to predict battery cell performance. The machine learning system includes a machine learning model that has been trained using training data includes test data of battery cells that reached respective end of life (EOL) cycles. In response, a prediction result for the battery cell is automatically generated by the machine learning model. The prediction result indicates an EOL cycle of the battery cell. An action is taken based on the prediction result.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery defect identification. One of the methods includes receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property in a battery test during at least one portion of a battery cycle. The battery test includes applying one or more pulses on the battery cell. The battery test data of the battery cell is provided as input to a machine learning model running on the computing system to predict whether the battery cell will experience catastrophic fade. The machine learning model has been trained using training data including battery test data of battery cells that experienced catastrophic fade. A prediction result for the battery cell is automatically generated by the machine learning model. An action is taken based on the prediction result for the battery cell.